Alzheimer's disease (AD) is commonly characterized by a loss of cognitive function due to the deterioration of neuronal synapses from the presence of senile amyloid beta-42 (Aß42) plaques. Evaluating cognitive deficits caused by Aß42 using human cortical neurons poses a challenge due to sourcing difficulties, and the use of animal models to assess drug efficacy creates biological hurdles from lack of species translatability. Recent advances in induced-pluripotent stem cell technology have enabled the development of mature, human-based cortical neuron models. The development of an hiPSC-cortical neuron differentiation protocol facilitates the exploration of disease onset and functional analysis from a patient-derived cell source, and further investigation of potential therapeutic treatments, while eliminating biological efficacy concerns. Long-term potentiation (LTP) was utilized as an in vitro correlate for memory and learning to quantify cognitive deficits in sporadic AD (SAD) and familial AD (FAD) systems and assess drug treatments for the prevention of Aß42-induced neurotoxicity. Synaptic connectivity and LTP induction through high-frequency stimulation was simulated through cortical neurons cultured on microelectrode arrays (MEAs), such that the functional activity of the neuronal population could be assessed overtime. AD therapeutic treatments were shown to block the Aß42-induced neurotoxic loss of synaptic plasticity and maintain persistent LTP in a model for SAD. Subsequently, FAD was assessed through the differentiation of patient-derived AD iPSCs, where LTP proficiency could be evaluated to relate to clinical cognitive evaluations. This study established a serum-free, in vitro human-derived iPSC-cortical neuron protocol that could be adapted to validate disease mechanisms and drug efficacy in patient-derived neural networks as a potential platform for precision medicine.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2174 |
| Date | 01 January 2022 |
| Creators | Autar, Kaveena |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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